CN109617135B - Power scheduling multi-objective decision method for hybrid power generation system - Google Patents

Power scheduling multi-objective decision method for hybrid power generation system Download PDF

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CN109617135B
CN109617135B CN201811600246.7A CN201811600246A CN109617135B CN 109617135 B CN109617135 B CN 109617135B CN 201811600246 A CN201811600246 A CN 201811600246A CN 109617135 B CN109617135 B CN 109617135B
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CN109617135A (en
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徐玖平
王凤娟
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Sichuan University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention relates to the technical field of power dispatching, and solves the problem that a decision maker is difficult to make a decision due to complex hybrid power dispatching. The technical scheme is summarized as follows: respectively establishing target models of power generation types contained in a decision model through a multi-objective decision form, setting parameter targets to be reached by the target models corresponding to various power generation types, integrating all the power generation types contained in the decision model, establishing a system target model of a hybrid power generation system, setting parameter targets to be reached by the system, forming a global model of power dispatching according to the target models of various power generation types and the target model of the power generation system, and inputting parameters related to the global model into the model for calculation so as to dispatch the hybrid power by a scheme meeting the requirements of the model. The beneficial effects are that: and a decision maker only needs to input required parameters into the model for calculation, so that the hybrid power is easier to schedule. The invention is particularly applicable to hybrid power systems.

Description

Power scheduling multi-objective decision method for hybrid power generation system
Technical Field
The invention relates to the technical field of power dispatching, in particular to a power dispatching technology of a hybrid power generation system.
Background
Thermal power is used as a traditional energy form, has high reliability and bears the most basic load, but a large amount of air pollutants are released in the coal combustion process, which causes great threat to the environment, and hydroelectric power, wind power and the like are introduced into a power generation system in the prior art to form a combined power generation system integrated by two or more than three of wind power, water power and thermal power. However, water, electricity and wind power are greatly influenced by natural conditions, the seasonal average wind speed of a wind power plant fluctuates greatly due to factors such as geographical positions and weather conditions, and the water inflow amount of the hydropower station in different seasons also varies due to factors such as rainfall conditions and storage capacity limitations. The strong dependence of wind power and hydropower on natural conditions obviously increases the complexity of power scheduling of the hybrid energy system, so that a decision maker is difficult to make a decision during hybrid power scheduling.
Disclosure of Invention
The invention provides a multi-objective decision method for power scheduling of a hybrid power generation system, aiming at solving the problem that a decision maker is difficult to decide due to complex hybrid power scheduling.
In order to solve the problems, the invention adopts the technical scheme that: the multi-objective decision method for power scheduling of the hybrid power generation system comprises the following steps:
establishing a decision model, wherein the decision model comprises at least two of a thermal power plant target model, a hydropower station target model and a wind power plant target model, and a power generation system target model;
the target model of the thermal power plant is as follows: in the current scheduling period, the carbon emission of the thermal power plant is less than or equal to alpha times of the carbon emission of the thermal power plant in a preset reference scheduling period;
the hydropower station target model is as follows: in the current scheduling period, the water abandoning amount of the hydropower station is less than or equal to beta times of the water abandoning amount of the hydropower station in a preset reference scheduling period;
the wind power plant target model is as follows: in the current scheduling period, the wind power utilization rate of the wind power plant is greater than or equal to lambda;
the power generation system target model is as follows: in the current scheduling period, the difference value between the total output of all corresponding power generation types contained in the decision model and the required power consumption in the scheduling period is less than or equal to delta;
inputting specific parameter data required in the decision model into the decision model for calculation, if all calculation results respectively meet corresponding conditions in the decision model, taking the power scheduling scheme corresponding to the specific parameter data input into the decision model at this time as a decision result of the current scheduling period, otherwise, taking the power scheduling scheme corresponding to the specific parameter data input into the decision model at this time as a decision result of the current scheduling period;
wherein alpha is more than 0, beta is more than 0, lambda is more than 0 and less than 1, and delta is more than or equal to 0.
As a further optimization, a plurality of wind speed scenes are set according to different wind speeds, and/or a plurality of water inflow scenes are set according to different water inflow, corresponding lambda and alpha values are set in each wind speed scene, or corresponding beta and alpha values are set in each water inflow scene, or corresponding lambda, beta and alpha values are set in a combined scene of each wind speed scene and each water inflow scene.
As a further optimization, the thermal power plant target model employs:
Figure BDA0001922299460000021
wherein I is the ith thermal power plant, I is the total number of the thermal power plants, J is the jth thermal power unit of the current thermal power plant, J is the total number of the thermal power units of the current thermal power plant, T is the tth unit time, T is the total number of the unit time of the current scheduling period, and SijCarbon emission coefficient, x, for burning coal for jth thermal power unit of ith thermal power plantijtThe weight of fire coal of the jth thermal power generating unit of the ith thermal power plant in the t unit time, IijtFor the jth thermal power generating unit of the ith thermal power plant at the tth unitThe on-off state, the power-on state and the power-off state in the interval are respectively represented by a number 1 and a number 0, CE is the carbon emission of the thermal power plant in the reference scheduling period, and I, J, T, I, J and T are positive integers.
As a further optimization, the thermal power plant target model further includes thermal power plant constraints, the thermal power plant constraints including:
coal available quantity constraint:
Figure BDA0001922299460000022
and (3) climbing restraint:
Figure BDA0001922299460000023
output restraint of the thermal power generating unit: pij-min≤Tijxijt≤Pij-max；
And (3) restraining the on-off state of the thermal power generating unit:
Figure BDA0001922299460000024
wherein the content of the first and second substances,
Figure BDA0001922299460000025
the maximum coal quantity T which can be used by the ith thermal power plant in the current scheduling periodijThe coal-electricity conversion coefficient of the jth thermal power generating unit of the ith thermal power plant,
Figure BDA0001922299460000026
the variation quantity between the upper limit of the ramp rate of the jth thermal power generating unit of the ith thermal power plant in the t unit time and the upper limit of the ramp rate of the thermal power generating unit in the t-1 unit time is calculated,
Figure BDA0001922299460000027
is the variable quantity P between the lower limit of the ramp rate of the jth thermal power unit of the ith thermal power plant in the t unit time and the lower limit of the ramp rate of the thermal power unit in the t-1 unit timeij-minThe minimum value of the jth thermal power generating unit of the ith thermal power plant in unit timeForce, Pij-maxIs the maximum output, T, of the jth thermal power unit of the ith thermal power plant in unit timeijt,onIs the online time T of the jth thermal power unit of the ith thermal power plant in the tth unit timeij,upIs the minimum on-line time T of the jth thermal power unit of the ith thermal power plant in unit timeijt,offIs the off-line time T of the jth thermal power generating unit of the ith thermal power plant in the tth unit timeij,downThe minimum off-line time of the jth thermal power generating unit of the ith thermal power plant in unit time.
As a further optimization, the hydropower station target model employs:
Figure BDA0001922299460000031
wherein M is the mth hydropower station, M is the total number of the hydropower stations, N is the nth hydroelectric generating set of the current hydropower station, N is the total number of the hydroelectric generating sets of the current hydropower station, T is the tth unit time, T is the total unit time of the current scheduling period, and Qmax,mMaximum water available for generating power for the mth hydropower station in the current scheduling period, ymntWater flow, g, of the nth hydroelectric power unit of the mth hydroelectric power station in the t unit timemntThe method is characterized in that the method is a startup and shutdown state of an nth hydroelectric generating set of an mth hydropower station in the tth unit time, the startup state and the shutdown state are respectively represented by a number 1 and a number 0, delta T is the unit time, CD is the water abandoning amount of the hydropower station in a reference scheduling period, and M, N, T, M, N and T are positive integers.
As a further optimization, the hydropower station target model further includes hydropower station constraints, the hydropower station constraints including:
and (3) restricting available water quantity:
Figure BDA0001922299460000032
and (3) water flow restriction of the hydroelectric generating set: qmin-mn≤ymnt≤Qmax-mn；
Reservoir dynamic water storage restraint:
Figure BDA0001922299460000033
wherein Q ismin,mMinimum water available for the mth hydropower station to generate electricity, Q, in the current scheduling periodmin-mnMinimum water flow rate, Q, allowed for the nth hydroelectric generating set of the mth hydroelectric power stationmax-mnMaximum water flow rate permitted for the nth hydroelectric generating set of the mth hydroelectric power station, AmtThe water storage capacity of the reservoir corresponding to the mth hydropower station at the end of the tth unit time, qmtThe water inlet speed of the reservoir corresponding to the mth hydropower station in the tth unit time, AmcAnd the water storage capacity of the reservoir corresponding to the mth hydropower station at the beginning of the current dispatching cycle.
As a further optimization, the wind farm target model employs:
Figure BDA0001922299460000041
wherein K is the kth wind power plant, K is the total number of the wind power plants, T is the tth unit time, T is the total number of unit times of the current scheduling period, and SkNumber of sample wind turbines for kth wind farm, ZktNumber of conventional wind turbines, L, operated for the kth wind farm in the t unit timekNumber of conventional wind turbines for the kth wind farm, Ptur,ktAnd K, K, T and T are positive integers, wherein the output curve of the wind turbine generator of the kth wind power plant is shown.
As a further optimization, the wind farm target model further includes wind farm constraints, including:
and (3) output curve constraint of the wind turbine generator:
Figure BDA0001922299460000042
the number of the wind turbine generators is restricted: zkt≤Lk；
Wherein v isktFor the wind speed, V, of the kth wind farm in the t unit timein,kIs as followsCut-in wind speed, V, of wind turbines of k wind farmsrated,kRated wind speed, V, of a wind turbine of the kth wind farmout,kCut-out wind speed, P, for the wind turbine of the kth wind farmrated,kThe rated power of the wind turbine of the kth wind power plant.
As a further optimization, the power generation system target model employs:
when the decision model comprises a thermal power plant target model and a hydropower station target model, the power generation system target model adopts the following steps:
Figure BDA0001922299460000051
when the decision model comprises a thermal power plant target model and a wind power plant target model, the power generation system target model adopts the following steps:
Figure BDA0001922299460000052
when the decision model comprises a hydropower station target model and a wind farm target model, the power generation system target model adopts the following steps:
Figure BDA0001922299460000053
when the decision model comprises a thermal power plant target model, a hydropower station target model and a wind farm target model, the power generation system target model adopts the following steps:
Figure BDA0001922299460000054
wherein, PctThe output of the thermal power plant in unit time, PhtThe output of the hydropower station in unit time, PwtThe output of the wind power plant in unit time, PdtT and T are positive integers for the required power consumption in unit time.
As a further optimization, the power generation system target model further includes a system rotation backup constraint that employs:
Figure BDA0001922299460000055
wherein the content of the first and second substances,
Figure BDA0001922299460000056
the maximum output of the thermal power plant in unit time,
Figure BDA0001922299460000057
is the minimum output of the thermal power plant in unit time,
Figure BDA0001922299460000058
the maximum output of the hydropower station in unit time,
Figure BDA0001922299460000059
the minimum output of the hydropower station in unit time is defined as a%, and the a% is the preset system rotation utilization rate.
The beneficial effects are that: the invention establishes target models of power generation types contained in a decision model respectively through a multi-objective decision form, sets parameter targets to be reached by the target models corresponding to various power generation types, integrates all the power generation types contained in the decision model, establishes a system target model of a hybrid power generation system, sets parameter targets to be reached by the system, forms a global model of power dispatching, namely the decision model, according to the target models of various power generation types and the target model of the power generation system, inputs corresponding specific parameter data of a current dispatching cycle into the decision model for calculation during hybrid power dispatching, and as long as all calculation results respectively meet corresponding conditions in the decision model, a dispatching scheme corresponding to the specific parameter data used in the calculation can be used as a power dispatching scheme of the current dispatching cycle, and further can calculate the output of various power generation types under the dispatching scheme, and the like, the hybrid power dispatching is realized, and a decision maker only needs to input required parameters into the model for calculation, so that the hybrid power dispatching is easier.
Detailed Description
The technical scheme of the invention is further explained by combining the embodiment.
The technical scheme of the invention is as follows:
the multi-objective decision method for power scheduling of the hybrid power generation system comprises the following steps:
establishing a decision model, wherein the decision model comprises at least two of a thermal power plant target model, a hydropower station target model and a wind power plant target model, and a power generation system target model;
the target model of the thermal power plant is as follows: in the current scheduling period, the carbon emission of the thermal power plant is less than or equal to alpha times of the carbon emission of the thermal power plant in a preset reference scheduling period;
the hydropower station target model is as follows: in the current scheduling period, the water abandoning amount of the hydropower station is less than or equal to beta times of the water abandoning amount of the hydropower station in a preset reference scheduling period;
the wind power plant target model is as follows: in the current scheduling period, the wind power utilization rate of the wind power plant is greater than or equal to lambda;
the power generation system target model is as follows: in the current scheduling period, the difference value between the total output of all corresponding power generation types contained in the decision model and the required power consumption in the scheduling period is less than or equal to delta;
inputting specific parameter data required in the decision model into the decision model for calculation, if all calculation results respectively meet corresponding conditions in the decision model, taking the power scheduling scheme corresponding to the specific parameter data input into the decision model at this time as a decision result of the current scheduling period, otherwise, taking the power scheduling scheme corresponding to the specific parameter data input into the decision model at this time as a decision result of the current scheduling period;
wherein alpha is more than 0, beta is more than 0, lambda is more than 0 and less than 1, and delta is more than or equal to 0.
The method comprises the steps of respectively establishing target models of power generation types contained in a decision model through a multi-objective decision form, setting parameter targets to be reached by the target models corresponding to various power generation types, integrating all the power generation types contained in the decision model, establishing a system target model of the hybrid power generation system, setting parameter targets to be reached by the system, forming a global model of power scheduling, namely the decision model, according to the target models of various power generation types and the target model of the power generation system, inputting corresponding specific parameter data of a current scheduling period into the decision model for calculation during hybrid power scheduling, and calculating a scheduling scheme corresponding to the specific parameter data used in the calculation as a power scheduling scheme of the current scheduling period as long as all calculation results respectively meet corresponding conditions in the decision model, further calculating the output of various power generation types under the scheduling scheme, and the like, and scheduling of hybrid power is realized.
As a further optimization, a plurality of wind speed scenes are set according to different wind speeds, and/or a plurality of water inflow scenes are set according to different water inflow, corresponding lambda and alpha values are set in each wind speed scene, or corresponding beta and alpha values are set in each water inflow scene, or corresponding lambda, beta and alpha values are set in a combined scene of each wind speed scene and each water inflow scene.
In the decision model, a thermal power plant target model aims at carbon emission, the carbon emission of a current scheduling period is based on the carbon emission of a reference scheduling period, the carbon emission needs to be made as small as possible, a hydropower station target model aims at water abandonment, the water abandonment of the current scheduling period is based on the water abandonment of the reference scheduling period, the water abandonment needs to be made as small as possible, a wind power plant target model aims at wind power utilization rate, the wind power utilization rate required to be achieved in the current scheduling period is set, the wind power utilization rate needs to be made as large as possible, a power generation system target model aims at a difference value between total power generation types and power demand consumption, and supply and demand are balanced as possible by setting a threshold value of the difference value. Wherein alpha, beta and lambda are comprehensively determined by a decision maker according to the power utilization plan, the carbon emission limit, the available water quantity, the incoming wind condition and the like of the current scheduling period; the selection of the reference scheduling period is the use of recent historical data and empirical data, for example, yesterday can be selected as the reference scheduling period when the decision maker is to schedule today.
The method is further optimized, and specifically, the method comprises the following steps:
the thermal power plant target model may employ:
Figure BDA0001922299460000071
wherein I is the ith thermal power plant, I is the total number of the thermal power plants, J is the jth thermal power unit of the current thermal power plant, J is the total number of the thermal power units of the current thermal power plant, T is the tth unit time, T is the total number of the unit time of the current scheduling period, and SijCarbon emission coefficient, x, for burning coal for jth thermal power unit of ith thermal power plantijtThe weight of fire coal of the jth thermal power generating unit of the ith thermal power plant in the t unit time, IijtThe method comprises the steps that the starting state and the shutdown state of a jth thermal power unit of an ith thermal power plant in the tth unit time are respectively represented by a number 1 and a number 0, CE is the carbon emission of the thermal power plant in a reference scheduling period, and I, J, T, I, J and T are positive integers. Through the thermal power plant target model, the total carbon emission of all thermal power generating units of all thermal power plants in the current scheduling period can be calculated, and then the total carbon emission is compared with the reference period so as to meet the setting requirement of the carbon emission.
In the thermal power plant target model, the carbon emission coefficient of the thermal power plant cannot be directly obtained, possibly due to the influence of lack of historical data, artificial factors and the like, and the parameter can be fixed in a relatively small range through investigation of experienced engineers and workers. Carbon emission coefficient S of coal burned by jth thermal power generating unit of ith thermal power plantijThe calculation method of (2) can adopt:
Figure BDA0001922299460000081
wherein the content of the first and second substances,
Figure BDA0001922299460000082
is the trapezoidal fuzzy number of the carbon emission coefficient of the jth thermal power unit of the ith thermal power plant,
Figure BDA0001922299460000083
rij1the minimum value of the carbon emission coefficient r of the j thermal power generating unit of the ith thermal power plant for burning coalij4The maximum value of the carbon emission coefficient r of the j thermal power generating unit of the ith thermal power plant for burning coalij2And rij3The carbon emission coefficient for burning coal for the jth thermal power unit of the ith thermal power plant is rij1And rij4A value of r betweenij1＜rij2＜rij3＜rij4，rij1、rij2、rij3 and rij4Namely the parameters obtained by the investigation are obtained,
Figure BDA0001922299460000084
is a pre-set attitude parameter, and is,
Figure BDA0001922299460000085
the thermal power plant target model may further include thermal power plant constraints, and the thermal power plant constraints may include:
coal available quantity constraint:
Figure BDA0001922299460000086
and (3) climbing restraint:
Figure BDA0001922299460000087
output restraint of the thermal power generating unit: pij-min≤Tijxijt≤Pij-max；
And (3) restraining the on-off state of the thermal power generating unit:
Figure BDA0001922299460000088
wherein the content of the first and second substances,
Figure BDA0001922299460000089
the maximum coal quantity T which can be used by the ith thermal power plant in the current scheduling periodijFor coal of j thermal power generating unit of i thermal power plantThe electrical conversion coefficient of the electric power converter,
Figure BDA00019222994600000810
the variation quantity between the upper limit of the ramp rate of the jth thermal power generating unit of the ith thermal power plant in the t unit time and the upper limit of the ramp rate of the thermal power generating unit in the t-1 unit time is calculated,
Figure BDA00019222994600000811
is the variable quantity P between the lower limit of the ramp rate of the jth thermal power unit of the ith thermal power plant in the t unit time and the lower limit of the ramp rate of the thermal power unit in the t-1 unit timeij-minIs the minimum output, P, of the jth thermal power unit of the ith thermal power plant in unit timeij-maxIs the maximum output, T, of the jth thermal power unit of the ith thermal power plant in unit timeijt,onIs the online time T of the jth thermal power unit of the ith thermal power plant in the tth unit timeij,upIs the minimum on-line time T of the jth thermal power unit of the ith thermal power plant in unit timeijt,offIs the off-line time T of the jth thermal power generating unit of the ith thermal power plant in the tth unit timeij,downThe minimum off-line time of the jth thermal power generating unit of the ith thermal power plant in unit time. After the constraint conditions are added into the model, the calculation result can be more consistent with the actual situation, and the calculation result is more accurate.
In the thermal power plant constraint, the coal-electricity conversion coefficient of the thermal power generating unit cannot be directly obtained, possibly due to the influence of lack of historical data, artificial factors and the like, and the parameter can be fixed in a relatively small range through investigation of experienced engineers and workers. Coal-electricity conversion coefficient T of j thermal power generating unit of i thermal power plant for burning coalijThe calculation method of (2) can adopt:
Figure BDA0001922299460000091
wherein the content of the first and second substances,
Figure BDA0001922299460000092
for the jth thermal power plant of the ith thermal power plantThe coal-electricity conversion coefficient of the coal-electricity conversion coefficient is a trapezoidal fuzzy number,
Figure BDA0001922299460000093
nij1the minimum value of the coal-electricity conversion coefficient, n, of the j thermal power generating unit of the ith thermal power plant for burning coalij4The maximum value of the coal-electricity conversion coefficient, n, for the j thermal power generating unit of the ith thermal power plant to burn coalij2And nij3The coal-electricity conversion coefficient for burning coal for the jth thermal power generating unit of the ith thermal power plant is nij1And nij4A value of between, nij1＜nij2＜nij3＜nij4，nij1、nij2、nij3And nij4Namely the parameters obtained by the investigation are obtained,
Figure BDA0001922299460000094
is a pre-set attitude parameter, and is,
Figure BDA0001922299460000095
the hydropower station target model may employ:
Figure BDA0001922299460000096
wherein M is the mth hydropower station, M is the total number of the hydropower stations, N is the nth hydroelectric generating set of the current hydropower station, N is the total number of the hydroelectric generating sets of the current hydropower station, T is the tth unit time, T is the total unit time of the current scheduling period, and Qmax,mMaximum water available for generating power for the mth hydropower station in the current scheduling period, ymntWater flow, g, of the nth hydroelectric power unit of the mth hydroelectric power station in the t unit timemntThe method is characterized in that the method is a startup and shutdown state of an nth hydroelectric generating set of an mth hydropower station in the tth unit time, the startup state and the shutdown state are respectively represented by a number 1 and a number 0, delta T is the unit time, CD is the water abandoning amount of the hydropower station in a reference scheduling period, and M, N, T, M, N and T are positive integers. By the hydropower station target model, all water in the current scheduling period can be calculatedAnd comparing the total water abandon amount of all the hydroelectric generating sets of the power station with a reference period to meet the set requirement of the water abandon amount.
The hydropower station target model may further include hydropower station constraints, and the hydropower station constraints may include:
and (3) restricting available water quantity:
Figure BDA0001922299460000101
and (3) water flow restriction of the hydroelectric generating set: qmin-mn≤ymnt≤Qmax-mn；
Reservoir dynamic water storage restraint:
Figure BDA0001922299460000102
wherein Q ismin,mMinimum water available for the mth hydropower station to generate electricity, Q, in the current scheduling periodmax,mCan adopt
Figure BDA0001922299460000103
Qmin-mnMinimum water flow rate, Q, allowed for the nth hydroelectric generating set of the mth hydroelectric power stationmax-mnMaximum water flow rate permitted for the nth hydroelectric generating set of the mth hydroelectric power station, AmtThe water storage capacity of the reservoir corresponding to the mth hydropower station at the end of the tth unit time, qmtThe water inlet speed of the reservoir corresponding to the mth hydropower station in the tth unit time, AmcAnd the water storage capacity of the reservoir corresponding to the mth hydropower station at the beginning of the current dispatching cycle. After the constraint conditions are added into the model, the calculation result can be more consistent with the actual situation, and the calculation result is more accurate.
The wind farm target model may employ:
Figure BDA0001922299460000104
wherein K is the kth wind power plant, K is the total number of the wind power plants, T is the T unit time, and T is the unit time of the current scheduling cycleTotal number, SkNumber of sample wind turbines for kth wind farm, ZktNumber of conventional wind turbines, L, operated for the kth wind farm in the t unit timekNumber of conventional wind turbines for the kth wind farm, Ptur,ktAnd K, K, T and T are positive integers, wherein the output curve of the wind turbine generator of the kth wind power plant is shown. Through the wind power plant target model, the total wind power utilization rate of all wind power plants in the current scheduling period can be calculated, so that the setting requirement of the wind power utilization rate is met.
The wind farm target model may further include wind farm constraints, and the wind farm constraints may include:
and (3) output curve constraint of the wind turbine generator:
Figure BDA0001922299460000111
the number of the wind turbine generators is restricted: zkt≤Lk；
Wherein v isktFor the wind speed, V, of the kth wind farm in the t unit timein,kFor the cut-in wind speed, V, of the wind turbine of the kth wind farmrated,kRated wind speed, V, of a wind turbine of the kth wind farmout,kCut-out wind speed, P, for the wind turbine of the kth wind farmrated,kThe rated power of the wind turbine of the kth wind power plant. After the constraint conditions are added into the model, the calculation result can be more consistent with the actual situation, and the calculation result is more accurate.
The power generation system target model may employ:
when the decision model comprises a thermal power plant target model and a hydropower station target model, the power generation system target model adopts the following steps:
Figure BDA0001922299460000112
when the decision model comprises a thermal power plant target model and a wind power plant target model, the power generation system target model adopts the following steps:
Figure BDA0001922299460000113
when the decision model comprises a hydropower station target model and a wind farm target model, the power generation system target model adopts the following steps:
Figure BDA0001922299460000114
when the decision model comprises a thermal power plant target model, a hydropower station target model and a wind farm target model, the power generation system target model adopts the following steps:
Figure BDA0001922299460000121
wherein, PctThe output of the thermal power plant in unit time, PhtThe output of the hydropower station in unit time, PwtThe output of the wind power plant in unit time, PdtT and T are positive integers for the required power consumption in unit time. The above power generation system target model sets δ to 0, making the power supply and power demand perfectly matched. Pct、PhtAnd PwtThe calculation method of (a) may specifically adopt:
Figure BDA0001922299460000122
Figure BDA0001922299460000123
Figure BDA0001922299460000124
above HmIs the head of the mth hydropower station, etamIs the overall efficiency coefficient of all hydroelectric generating sets of the mth hydropower station, and 9.81 is the gravity coefficient of water, with the unit of kN/m3。
The power generation system target model may further include a system rotation backup constraint, and the system rotation backup constraint may adopt:
Figure BDA0001922299460000125
wherein the content of the first and second substances,
Figure BDA0001922299460000126
the maximum output of the thermal power plant in unit time,
Figure BDA0001922299460000127
is the minimum output of the thermal power plant in unit time,
Figure BDA0001922299460000128
the maximum output of the hydropower station in unit time,
Figure BDA0001922299460000129
the minimum output of the hydropower station in unit time is defined as a%, and the a% is the preset system rotation utilization rate. By setting the spare capacity for the system, the power system can still ensure the supply of power under the conditions of equipment maintenance, accidents, frequency modulation and the like.
Figure BDA00019222994600001210
And
Figure BDA00019222994600001211
the calculation method of (a) may specifically adopt:
Figure BDA00019222994600001212
Figure BDA0001922299460000131
Figure BDA0001922299460000132
Figure BDA0001922299460000133
examples
The following specifically exemplifies the technical solution of the present invention. The power dispatching multi-objective decision method of the hybrid power generation system is based on a hybrid power generation system of thermal power, hydroelectric power and wind power, and the system comprises a first hydropower station, a first thermal power plant, a second thermal power plant, a first wind power plant and a second wind power plant.
The decision model established in this example is specifically:
Figure BDA0001922299460000134
Figure BDA0001922299460000141
Figure BDA0001922299460000151
in the decision model, I is the ith thermal power plant, I is the total number of the thermal power plants, J is the jth thermal power unit of the current thermal power plant, J is the total number of the thermal power units of the current thermal power plant, T is the tth unit time, T is the total number of the unit times of the current scheduling period, and SijCarbon emission coefficient, x, for burning coal for jth thermal power unit of ith thermal power plantijtThe weight of fire coal of the jth thermal power generating unit of the ith thermal power plant in the t unit time, IijtThe starting state and the shutdown state of the jth thermal power unit of the ith thermal power plant in the t unit time are respectively represented by a numeral 1 and a numeral 0, CE is the carbon emission of the thermal power plant in the reference scheduling period,
Figure BDA0001922299460000152
is the ithTrapezoidal fuzzy number r of carbon emission coefficient of jth thermal power unit of thermal power plantij1The minimum value of the carbon emission coefficient r of the j thermal power generating unit of the ith thermal power plant for burning coalij4The maximum value of the carbon emission coefficient r of the j thermal power generating unit of the ith thermal power plant for burning coalij2And rij3The carbon emission coefficient for burning coal for the jth thermal power unit of the ith thermal power plant is rij1And rij4A value of r betweenij1＜rij2＜rij3＜rij4，
Figure BDA0001922299460000153
Is a pre-set attitude parameter, and is,
Figure BDA0001922299460000154
Figure BDA0001922299460000155
the maximum coal quantity T which can be used by the ith thermal power plant in the current scheduling periodijThe coal-electricity conversion coefficient of the jth thermal power generating unit of the ith thermal power plant,
Figure BDA0001922299460000161
the variation quantity between the upper limit of the ramp rate of the jth thermal power generating unit of the ith thermal power plant in the t unit time and the upper limit of the ramp rate of the thermal power generating unit in the t-1 unit time is calculated,
Figure BDA0001922299460000162
is the variable quantity P between the lower limit of the ramp rate of the jth thermal power unit of the ith thermal power plant in the t unit time and the lower limit of the ramp rate of the thermal power unit in the t-1 unit timeij-minIs the minimum output, P, of the jth thermal power unit of the ith thermal power plant in unit timeij-maxIs the maximum output, T, of the jth thermal power unit of the ith thermal power plant in unit timeijt,onIs the online time T of the jth thermal power unit of the ith thermal power plant in the tth unit timeij,upThe minimum online time of the jth thermal power generating unit of the ith thermal power plant in unit time,Tijt,offis the off-line time T of the jth thermal power generating unit of the ith thermal power plant in the tth unit timeij,downThe minimum off-line time of the jth thermal power generating unit of the ith thermal power plant in unit time,
Figure BDA0001922299460000163
is the coal-electricity conversion coefficient trapezoidal fuzzy number n of the jth thermal power generating unit of the ith thermal power plantij1The minimum value of the coal-electricity conversion coefficient, n, of the j thermal power generating unit of the ith thermal power plant for burning coalij4The maximum value of the coal-electricity conversion coefficient, n, for the j thermal power generating unit of the ith thermal power plant to burn coalij2And nij3The coal-electricity conversion coefficient for burning coal for the jth thermal power generating unit of the ith thermal power plant is nij1And nij4A value of between, nij1＜nij2＜nij3＜nij4，
Figure BDA0001922299460000164
Is a pre-set attitude parameter, and is,
Figure BDA0001922299460000165
m is the mth hydropower station, M is the total number of the hydropower stations, N is the nth hydroelectric generating set of the current hydropower station, N is the total number of the hydroelectric generating sets of the current hydropower station, Qmax,mMaximum water available for generating power for the mth hydropower station in the current scheduling period, ymntWater flow, g, of the nth hydroelectric power unit of the mth hydroelectric power station in the t unit timemntThe startup and shutdown states of the nth hydroelectric generating set of the mth hydropower station in the tth unit time are respectively represented by a numeral 1 and a numeral 0, delta t is the unit time, CD is the water abandoning amount of the hydropower station in the reference dispatching period, and Qmin,mMinimum water available for the mth hydropower station to generate electricity, Q, in the current scheduling periodmin-mnMinimum water flow rate, Q, allowed for the nth hydroelectric generating set of the mth hydroelectric power stationmax-mnMaximum water flow rate permitted for the nth hydroelectric generating set of the mth hydroelectric power station, AmtStorage of the reservoir corresponding to the mth hydropower station at the end of the tth unit timeAmount of water, qmtThe water inlet speed of the reservoir corresponding to the mth hydropower station in the tth unit time, AmcThe water storage capacity of a reservoir corresponding to the mth hydropower station at the beginning of the current scheduling period is represented by K, K is the kth wind power plant, K is the total number of the wind power plants, and SkNumber of sample wind turbines for kth wind farm, ZktNumber of conventional wind turbines, L, operated for the kth wind farm in the t unit timekNumber of conventional wind turbines for the kth wind farm, Ptur,ktIs the output curve, v, of the wind turbine of the kth wind farmktFor the wind speed, V, of the kth wind farm in the t unit timein,kFor the cut-in wind speed, V, of the wind turbine of the kth wind farmrated,kRated wind speed, V, of a wind turbine of the kth wind farmout,kCut-out wind speed, P, for the wind turbine of the kth wind farmrated,kRated power, P, of wind turbine of the kth wind farmctThe output of the thermal power plant in unit time, PhtThe output of the hydropower station in unit time, PwtThe output of the wind power plant in unit time, PdtFor the required power consumption per unit time, HmIs the head of the mth hydropower station, etamIs the overall efficiency coefficient of all hydroelectric generating sets of the mth hydropower station, and 9.81 is the gravity coefficient of water, with the unit of kN/m3，
Figure BDA0001922299460000171
The maximum output of the thermal power plant in unit time,
Figure BDA0001922299460000172
is the minimum output of the thermal power plant in unit time,
Figure BDA0001922299460000173
the maximum output of the hydropower station in unit time,
Figure BDA0001922299460000174
the minimum output of the hydropower station in unit time is defined, and a% is a preset system rotation utilization rate, i, j, t, m,N, K, I, J, M, N, K and T are all positive integers.
Setting 4 wind speed scenes of spring, summer, autumn and winter according to different wind speeds, setting 3 water inflow scenes of rich water, flat water and dry water according to different water inflow, further combining to form 12 different scheduling scenes, and setting corresponding lambda, beta and alpha values under each scheduling scene, wherein the current scheduling period is in the summer flat period scene as an example, alpha is set to 0.965, beta is set to 0.95, and lambda is set to 0.5 under the scene; the current scheduling period takes 24 hours, and the unit time takes 1 hour.
The specific parameter data calculated in this example are shown in tables 1-11.
TABLE 1A parameter table for hydropower stations
Figure BDA0001922299460000175
Figure BDA0001922299460000181
TABLE 2 parameter table of hydroelectric generating set of first hydropower station
Figure BDA0001922299460000182
TABLE 3 wind farm parameter table
Figure BDA0001922299460000183
TABLE 4 wind farm two-parameter TABLE
Figure BDA0001922299460000184
TABLE 5 fixed parameter tables for thermal power plant I and thermal power plant II
Figure BDA0001922299460000191
TABLE 6 fuzzy parameter tables for thermal power plant I and thermal power plant II
Figure BDA0001922299460000192
TABLE 7 anemometer of wind farm in each unit time in current scheduling period
Figure BDA0001922299460000193
TABLE 8 wind speed table of wind farm II in each unit time in the current scheduling period
Figure BDA0001922299460000201
Table 9 is the power consumption demand table of each unit time in the current scheduling period
Figure BDA0001922299460000202
Table 10 water intake velocity meter of reservoir of hydropower station i in each unit time in current dispatching cycle
Figure BDA0001922299460000203
Table 11 shows the weight of the fire coal of each thermal power generating unit in each unit time, the water flow of each hydroelectric power generating unit in each unit time, and the number of the conventional wind power generating units operated in each unit time in each wind farm in the current scheduling period
Figure BDA0001922299460000211
Figure BDA0001922299460000221
The parameters required for the calculation in this example are also: the carbon emission of the selected reference scheduling period is 19987 kilograms, and the water discard amount is 16 multiplied by 105Cubic meter; all thermal power generating units are in a starting state in the whole process; the maximum coal quantity which can be used by all thermal power plants is assumed to be infinite;
Figure BDA0001922299460000231
the method adopts the technical scheme that the reaction time of the catalyst is 0.5,
Figure BDA0001922299460000232
0.5 is adopted; setting the minimum water quantity which can be used for generating power in the current scheduling period of the hydropower station I as 0; when the water flow of the hydroelectric generating set in unit time is 0, the hydroelectric generating set is in a shutdown state in the unit time, otherwise, the hydroelectric generating set is in a startup state; the water storage capacity of the reservoir corresponding to the first hydropower station at the beginning of the current dispatching cycle adopts the normal reservoir capacity value of the first hydropower station; the water head of the first hydropower station adopts a rated water head of a hydroelectric generating set of the first hydropower station; the system rotation standby rate is 5%.
The data are input into a decision model for calculation, and the obtained results are shown in tables 12 to 14.
TABLE 12 output table of hydropower station in each unit time
Figure BDA0001922299460000233
Table 13 output meter of wind power station in each unit time
Figure BDA0001922299460000234
Table 14 is the output of the thermal power plant in units of MW at each unit time.
Figure BDA0001922299460000235
Figure BDA0001922299460000241
The carbon emission of the current scheduling period is 17478 kg and the water discard amount is 9 multiplied by 10 by calculation5Cubic meter, wind power utilization rate is 0.55. It can be seen from the above data that all the calculation results respectively satisfy the corresponding conditions in the decision model, for example, the carbon emission of the current scheduling cycle is less than 0.965 times of the carbon emission of the reference scheduling cycle, the water abandon amount of the current scheduling cycle is less than 0.95 times of the water abandon amount of the reference scheduling cycle, the wind power utilization rate of the current scheduling cycle is greater than 0.5, and so on, therefore, the power scheduling scheme corresponding to the specific parameter data input into the decision model at this time can be used as the decision result of the current scheduling cycle, and the power scheduling scheme of the current scheduling cycle can be operated according to the data of each generator set input at this time in each unit time.

Claims (5)

1. The multi-objective decision making method for power scheduling of the hybrid power generation system is characterized by comprising the following steps of:
establishing a decision model, wherein the decision model comprises at least two of a thermal power plant target model, a hydropower station target model and a wind power plant target model, and a power generation system target model;
the target model of the thermal power plant is as follows: in the current scheduling period, the carbon emission of the thermal power plant is less than or equal to alpha times of the carbon emission of the thermal power plant in a preset reference scheduling period;
the hydropower station target model is as follows: in the current scheduling period, the water abandoning amount of the hydropower station is less than or equal to beta times of the water abandoning amount of the hydropower station in a preset reference scheduling period;
the reference scheduling period is derived from recent historical data;
the wind power plant target model is as follows: in the current scheduling period, the wind power utilization rate of the wind power plant is greater than or equal to lambda;
the power generation system target model is as follows: in the current scheduling period, the difference value between the total output of all corresponding power generation types contained in the decision model and the required power consumption in the scheduling period is less than or equal to delta;
inputting specific parameter data required in the decision model into the decision model for calculation, if all calculation results respectively meet corresponding conditions in the decision model, taking the power scheduling scheme corresponding to the specific parameter data input into the decision model at this time as a decision result of the current scheduling period, otherwise, taking the power scheduling scheme corresponding to the specific parameter data input into the decision model at this time as a decision result of the current scheduling period;
wherein alpha is more than 0, beta is more than 0, lambda is more than 0 and less than 1, and delta is more than or equal to 0;
when the decision model comprises a thermal power plant target model and a hydropower station target model, the power generation system target model adopts the following steps:
Figure FDA0003289429200000011
when the decision model comprises a thermal power plant target model and a wind power plant target model, the power generation system target model adopts the following steps:
Figure FDA0003289429200000012
when the decision model comprises a hydropower station target model and a wind farm target model, the power generation system target model adopts the following steps:
Figure FDA0003289429200000013
when the decision model comprises a thermal power plant target model, a hydropower station target model and a wind farm target model, the power generation system target model adopts the following steps:
Figure FDA0003289429200000014
wherein, PctFor the presence of a thermal power plantForce in bit time, PhtThe output of the hydropower station in unit time, PwtThe output of the wind power plant in unit time, PdtThe required power consumption in unit time is shown, and T and T are positive integers;
the power generation system target model further includes a system rotation backup constraint that employs:
Figure FDA0003289429200000021
wherein the content of the first and second substances,
Figure FDA0003289429200000022
the maximum output of the thermal power plant in unit time,
Figure FDA0003289429200000023
is the minimum output of the thermal power plant in unit time,
Figure FDA0003289429200000024
the maximum output of the hydropower station in unit time,
Figure FDA0003289429200000025
the minimum output of the hydropower station in unit time is defined, and a% is a preset system rotation utilization rate;
the thermal power plant target model adopts:
Figure FDA0003289429200000026
wherein I is the ith thermal power plant, I is the total number of the thermal power plants, J is the jth thermal power unit of the current thermal power plant, J is the total number of the thermal power units of the current thermal power plant, T is the tth unit time, T is the total number of the unit time of the current scheduling period, and SijCarbon emission coefficient, x, for burning coal for jth thermal power unit of ith thermal power plantijtBurning coal of jth thermal power generating unit of ith thermal power plant in tth unit timeWeight, IijtThe method comprises the steps that the starting state and the shutdown state of a jth thermal power unit of an ith thermal power plant in the tth unit time are respectively represented by a number 1 and a number 0, CE is the carbon emission of the thermal power plant in a reference scheduling period, and I, J, T, I, J and T are positive integers;
the hydropower station target model adopts the following steps:
Figure FDA0003289429200000027
wherein M is the mth hydropower station, M is the total number of the hydropower stations, N is the nth hydroelectric generating set of the current hydropower station, N is the total number of the hydroelectric generating sets of the current hydropower station, T is the tth unit time, T is the total unit time of the current scheduling period, and Qmax,mMaximum water available for generating power for the mth hydropower station in the current scheduling period, ymntWater flow, g, of the nth hydroelectric power unit of the mth hydroelectric power station in the t unit timemntThe method comprises the steps that the method is a startup and shutdown state of an nth hydroelectric generating set of an mth hydropower station in the tth unit time, the startup state and the shutdown state are respectively represented by a number 1 and a number 0, delta T is the unit time, CD is the water abandoning amount of the hydropower station in a reference scheduling period, and M, N, T, M, N and T are positive integers;
the wind power plant target model adopts the following steps:
Figure FDA0003289429200000031
wherein K is the kth wind power plant, K is the total number of the wind power plants, T is the tth unit time, T is the total number of unit times of the current scheduling period, and SkNumber of sample wind turbines for kth wind farm, ZktNumber of conventional wind turbines, L, operated for the kth wind farm in the t unit timekNumber of conventional wind turbines for the kth wind farm, Ptur,ktAnd K, K, T and T are positive integers, wherein the output curve of the wind turbine generator of the kth wind power plant is shown.
2. The hybrid power generation system power scheduling multi-objective decision method of claim 1, further comprising: setting a plurality of wind speed scenes according to different wind speeds, and/or setting a plurality of water inflow scenes according to different water inflow, setting corresponding lambda and alpha values in each wind speed scene, or setting corresponding beta and alpha values in each water inflow scene, or setting corresponding lambda, beta and alpha values in a combined scene of each wind speed scene and each water inflow scene.
3. The hybrid power generation system power scheduling multi-objective decision method of claim 1, wherein the thermal power plant objective model further comprises thermal power plant constraints, the thermal power plant constraints comprising:
coal available quantity constraint:
Figure FDA0003289429200000032
and (3) climbing restraint:
Figure FDA0003289429200000033
unit output restraint: pij-min≤Tijxijt≤Pij-max；
And (3) restraining the on-off state:
Figure FDA0003289429200000034
wherein, Xi maxThe maximum coal quantity T which can be used by the ith thermal power plant in the current scheduling periodijThe coal-electricity conversion coefficient of the jth thermal power generating unit of the ith thermal power plant,
Figure FDA0003289429200000041
the variation quantity between the upper limit of the ramp rate of the jth thermal power generating unit of the ith thermal power plant in the t unit time and the upper limit of the ramp rate of the thermal power generating unit in the t-1 unit time is calculated,
Figure FDA0003289429200000042
for the ith fireThe variation, P, between the lower limit of the ramp rate of the jth thermal power generating unit in the t unit time and the lower limit of the ramp rate of the thermal power generating unit in the t-1 unit time in the power plantij-minIs the minimum output, P, of the jth thermal power unit of the ith thermal power plant in unit timeij-maxIs the maximum output, T, of the jth thermal power unit of the ith thermal power plant in unit timeijt,onIs the online time T of the jth thermal power unit of the ith thermal power plant in the tth unit timeij,upIs the minimum on-line time T of the jth thermal power unit of the ith thermal power plant in unit timeijt,offIs the off-line time T of the jth thermal power generating unit of the ith thermal power plant in the tth unit timeij,downThe minimum off-line time of the jth thermal power generating unit of the ith thermal power plant in unit time.
4. The hybrid power generation system power scheduling multi-objective decision method of claim 1, wherein the hydropower station objective model further comprises hydropower station constraints, the hydropower station constraints comprising:
and (3) restricting available water quantity:
Figure FDA0003289429200000043
and (3) water flow restriction of the hydroelectric generating set: qmin-mn≤ymnt≤Qmax-mn；
Reservoir dynamic water storage restraint:
Figure FDA0003289429200000044
wherein Q ismin,mMinimum water available for the mth hydropower station to generate electricity, Q, in the current scheduling periodmin-mnMinimum water flow rate, Q, allowed for the nth hydroelectric generating set of the mth hydroelectric power stationmax-mnMaximum water flow rate permitted for the nth hydroelectric generating set of the mth hydroelectric power station, AmtThe water storage capacity of the reservoir corresponding to the mth hydropower station at the end of the tth unit time, qmtThe water inlet speed of the reservoir corresponding to the mth hydropower station in the tth unit time, AmcAnd the water storage capacity of the reservoir corresponding to the mth hydropower station at the beginning of the current dispatching cycle.
5. The hybrid power generation system power scheduling multi-objective decision method of claim 1, wherein the wind farm target model further comprises wind farm constraints, the wind farm constraints comprising:
and (3) output curve constraint of the wind turbine generator:
Figure FDA0003289429200000051
the number of the wind turbine generators is restricted: zkt≤Lk；
Wherein v isktFor the wind speed, V, of the kth wind farm in the t unit timein,kFor the cut-in wind speed, V, of the wind turbine of the kth wind farmrated,kRated wind speed, V, of a wind turbine of the kth wind farmout,kCut-out wind speed, P, for the wind turbine of the kth wind farmrated,kThe rated power of the wind turbine of the kth wind power plant.
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